A groundbreaking digital twin technology developed at the University of Virginia is transforming how people with type 1 diabetes manage their condition. This innovation allows UVA’s artificial pancreas system to adjust to individual users’ changing needs while enabling them to personalize system settings. A recent study published in npj Digital Medicine confirms its effectiveness, showing improved blood sugar control among users.
Adaptive Biobehavioral Control Enhances Outcomes
At the core of this breakthrough lies a method called “adaptive biobehavioral control.” This feature refines the artificial pancreas settings every two weeks based on each user’s unique data. It creates a virtual testing space, allowing users to explore various blood sugar management strategies safely before applying them. In a six-month clinical study, participants using the system increased their time within a healthy blood sugar range from 72% to 77%. They also experienced a modest yet significant reduction in average glucose levels.
Digital Twins: Simulating the Body’s Response
As reported by Medicalxpress, the technology creates a “digital twin”—a computer-generated model of how an individual’s body processes sugar. This simulation lets the artificial pancreas system better respond to daily variations, such as those caused by meals or physical activity. Users can test changes—like adjusting overnight insulin delivery—within the virtual model before implementing them in reality.
Empowering Human-Machine Co-Adaptation
Dr. Boris Kovatchev, director of the UVA Center for Diabetes Technology, emphasizes the importance of synergy between human input and machine intelligence. “Artificial pancreas systems require ongoing adjustments to match a person’s changing insulin needs,” he explained. “Our system maps each user to their digital twin in the cloud, letting them safely simulate changes and understand how the system will respond.”
Looking Ahead
This approach promotes dynamic co-adaptation between the user and the technology. As Kovatchev notes, “Human-machine co-adaptation is essential for managing chronic conditions like type 1 diabetes. Digital-twin technology plays a crucial role in making this collaboration more intuitive and effective.”